Large-scale multi-input multi-output (MIMO) systems are attractive due to their high capacity and bandwidth efficiency. By transmitting and receiving signals via tens or hundreds of antennas, large-scale MIMO systems show great potential for next generation wireless communications, for example, to obtain high spectral efficiencies. However, a critical challenge in the design of large-scale MIMO systems is to provide high performance, high throughput, and low latency, while reducing the complexity of the detectors. Despite its optimal error performance, maximum likelihood detectors (MLD) require prohibitively high complexity, especially when the number of antennas is large.
Sphere decoding algorithm (SDA) is an MLD with less complexity, but the complexity of the SDA is still exponential in terms of problem size. To address the stringent needs of large-scale MIMO detection, several detectors for large-scale MIMO systems have been proposed.
For example, local neighborhood search methods are developed to obtain near-optimal performance for large-scale MIMO systems, but their complexity depends on symbol, noise, and channel realizations and their worst-case complexity can be extremely high. Iterative soft interference cancellation detectors have fixed complexity; however, the complexity is still high if the number of iterations is large. Furthermore, when the constellation size is large, the detector cannot collect full diversity as the MLD, thus suffering from inferior performance.
In contrast, linear detectors (LDs) and successive interference cancellation (SIC) detectors require polynomial complexity but suffer from significantly degraded error performance Recently, to improve the error performance of LDs and SIC detectors, lattice reduction (LR)-aided detection has been proposed. LR-aided LDs can achieve the same diversity as the MLD. In addition, different from the search-based detectors described, the instantaneous complexity of LR-aided detector does not depend on symbol and noise realizations, which is preferred for hardware implementation.
Although significant performance improvement for LR-aided LDs and SIC detectors is found, the LR-aided detectors still exhibit some performance loss to the MLD. In addition, as the number of antennas increases, the gap between the LR-aided detectors and the MLD increases significantly.
To further bridge the gap, LR-aided K-best detectors are proposed. Among existing MIMO detectors, LR-aided K-best detectors are attractive for their low complexity and (near-) optimal performance. However, compared to the conventional K-best detectors, the LR-aided K-best detector has no boundary information about the symbols in the lattice-reduced domain.
The loss of boundary information results in two new issues of LR-aided K-best detector relative to the conventional K-best: i) the range of the symbols is broader and undetermined and; ii) the possible children for each layer can be infinite. To find the K best partial candidates from the infinite children set, an algorithm is proposed to replace the infinite set with a finite subset of the children. See X. Qi and K. Holt, “A lattice-reduction-aided soft demapper for high-rate coded MEMO-OFDM systems,” IEEE Signal Process. Lett., vol. 14, no. 5, pp. 305-308, May 2007 (hereinafter “Qi”). To reduce the complexity of generating the subset, an on-demand child expansion based on the Schnorr-Euchner (SE) strategy is also proposed. See M. Shabany and P. Glenn Gulak, “The application of lattice-reduction to the K-Best algorithm for near-optimal MIMO detection,” in IEEE Int. Symp. on Circuits and Systems (ISCAS), May 2008, pp. 316-319 (hereinafter “Shabany”).
Nevertheless, the existing LR-aided K-best detectors still face several challenges in hardware implementation in terms of latency, throughput, and complexity. Most existing LR-aided K-best detectors consider real equivalent signal model of the complex model. For example, existing LR-aided K-best detectors may result in long latency and high hardware resources. On the other hand, the complex LR-aided K-best detectors may yield shorter latency and lower resources, but existing complex LR-aided K-best designs are complicated and may not be easy to implement in hardware. Further, the critical path of some existing K-best detectors is determined by the SE expansion, which may lower the maximum frequency and thus system throughput. Furthermore, the existing LR-aided K-best algorithm has a high complexity on the order of O(Nt2K+NtK2), where Nt is the number of transmit antennas and K is the number of candidates.
Based on the foregoing, there is a need for a less complex LR-aided K-best detector with low latency, high throughput and high performance.